眉毛
人工智能
模式识别(心理学)
计算机科学
面部识别系统
特征(语言学)
联营
卷积神经网络
特征提取
三维人脸识别
面子(社会学概念)
生物识别
计算机视觉
人脸检测
心理学
沟通
哲学
社会学
语言学
社会科学
作者
Baojin Huang,Zhongyuan Wang,Guangcheng Wang,Zhen Han,Kui Jiang
摘要
During the COVID-19 coronavirus epidemic, wearing masks has become increasingly popular. Traditional occlusion face recognition algorithms are almost ineffective for such heavy mask occlusion. Therefore, it is urgent to improve the recognition performance of the existing face recognition technology on masked faces. Due to the limited visible feature points of the masked face image relative to the normal face image, we have to exploit the identification potential of eyebrow (referring to eyes and brows) features. This article proposes a local eyebrow feature attention network for masked face recognition, which consists of feature extraction, eyebrow region pooling, and feature fusion. To highlight the eyebrow region, we first use the eyebrow region pooling to separate the local features of eyebrows from the learned overall facial features. We then make full use of the symmetry of left and right eyebrows to emphasize their discriminant ability, due to the inadequate fine information of the low-resolution eyebrows. In particular, in view of the symmetrical similarity between eyebrow pairs and the subordinate relationship between facial components and the whole, we propose a feature fusion model based on graph convolutional network (GCN) to learn the feature association structure of eye features, brow features, and global facial features. We construct the benchmark datasets for masked face recognition to validate our approach, including real-world masked face recognition dataset (RMFRD) and synthetic masked face recognition dataset (SMFRD). Extensive experimental results on both public datasets and our built masked face datasets show that our approach significantly outperforms the state-of-the-arts.
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